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UG :- - Not Required
PG :- - Not Required
No of position :- ( 1 )
Post :- 9th Jan 2024
Develop and Execute Data, Analytics, and AI Strategy:
Collaborate with key stakeholders to formulate a comprehensive Data, Analytics, and AI strategy aligned with the organization's objectives and long-term vision.
Drive the adoption of cutting-edge technologies and best practices in Data, Analytics, and AI to enhance business outcomes.
Oversee the development and implementation of scalable and secure data architecture and analytics solutions.
Collaborate closely with business units to grasp their data and analytics requirements and offer strategic advice.
Propel the integration of AI and Generative AI technologies to elevate business operations and drive innovation.
Identify emerging trends and technologies, evaluating their potential impact on the organization.
Take the lead in conducting proof of concepts using selected emerging trends and technologies, providing comprehensive reports to senior leaders.
Hands-on approach, be ready to write code as needed.
Cultivate collaboration and knowledge exchange across various teams and departments in the realm of Analytics.
Measure and report (through goals and KPIs) on the effectiveness of solution delivery
Architectural Leadership:
Establish and uphold architectural standards and best practices for Data, Analytics, and AI solutions.
Lead the design and implementation of scalable, secure, and high-performance data architecture and analytics platforms.
Business Collaboration:
Work closely with business units to understand their data and analytics requirements and provide strategic guidance.
Translate business needs into architectural blueprints that drive actionable insights and deliver value.
Innovation and Trends:
Identify emerging trends and technologies in the Data, Analytics, and AI space and assess their potential impact on the organization.
Lead proof of concepts with selected technologies and provide insightful reports to senior leadership.
Knowledge Sharing and Collaboration:
Foster a culture of collaboration and knowledge sharing across various teams and departments in the realm of Data, Analytics, and AI.
Establish and lead an analytics forum for technology leaders and developers to exchange ideas, communicate updates, and gather input for strategy refinement.
Vendor and Tool Selection:
Assist in the evaluation, recommendation, and selection of appropriate tools, platforms, and vendors for data and AI initiatives.
Ensure that chosen solutions align with architectural standards and organizational goals.
Team Leadership
Be able to mentor junior Data Science/AI SMEs around self.
Provide technical leadership.
Be able to work in a matrix, fast paced environment.
Maturity to handle ambiguous situations
Required Skills and Qualifications:
Technical Proficiency:
Demonstrated expertise in Data, Analytics, and AI technologies and methodologies.
Proven ability to provide solutions to complex technical challenges and drive innovation.
Leadership and Vision:
Strong leadership skills with the ability to provide technical direction and vision for the organization.
Track record of driving technical excellence and adopting new technologies to solve business problems.
Architecture and Design:
Extensive experience in designing and implementing scalable, secure, and performant data architecture and analytics solutions.
Proficiency in software product design and architecture.
Business Assessment:
Ability to assess the benefits, risks, and success factors of potential applications in the Data, Analytics, and AI space.
Experience in developing best practices for business assessment and validation.
Communication and Collaboration:
Excellent communication skills with the ability to convey technical concepts to non-technical stakeholders.
Proven ability to collaborate effectively with cross-functional teams and business units.
Technical Stack
Fluency and experience in Python, Data Science, Data Engineering & MLOPS
Knowledge in RESTful API design and implementation
Experience of Web framework like FastAPI/Tornado/Flask etc.
Data Science knowledge and familiarity with ML libraries such as Pandas, Scikit, TensorFlow, xgboost, time series frameworks like prophet/or equivalent frameworks
In-depth of big data frameworks like Pyspark & gcp tools like data proc, data proc serverless
Experience designing, building and operating productions grade ML applications
Knowledge of design patterns and architecture, data science, and machine learning best practices
Working knowledge of ML frameworks, such as Vertex, Kubeflow, MLflow, CloudRun etc.
Experience in designing post-deployment model management framework e.g. model monitoring tools, workflows for feature drift, error analysis of models
Experience in designing MLOps platforms and architect big data systems on GCP cloud
Experience with relational databases like Big Query, cloud environments, and a good understanding of optimizing storage cost/query cost while designing data engineering workflows
Good knowledge of Kubernetes, container technologies, docker registries, and applying them in the context of machine learning systems
Proficiency with CI/CD tools, especially Jenkins
Experience in designing CI/CD pipelines (Jenkins) for deployment of Data Engineering and ML jobs workflow
Hands-on experience in orchestration frameworks like Airflow, Cloud Composer, DataProc Serverless for Pyspark jobs etc.
In-depth understanding of Google Cloud ecosystem for Data Engineering & MLOps - cloud composer, dataproc, dataproc serverless, big query, cloud run, vertex, vertex pipelines, GKE
Education and Experience:
Bachelor's or higher degree in Computer Science / Computer Engineering / Statistics / Data Science
15+ years of experience in Enterprise Architecture, with a focus on AI , Data & Analytics